giotto-ai/football-tda

The shape of football games.

football-tda's Issues

cross_validation.py has problems when ran as main.

In cross_validation.py, CrossValidate.cross_validate() has comparison between strings and pd.Timestamp (lines 140-147) if called as main.
One can add

df['date'] = pd.to_datetime(df['date'])

on line 234.

The function extract_features_for_prediction doesn't work when cross_validation.py is ran as main, because it attempts to stack the train dataset with topological features to the test dataset.
Substitute line 36 including only the non-topological features:

all_x_train = x_train[:, :14]

Finally, on line 200, one should have

model_config_with_topo = self._validate_k_fold_model(x_train_model[:train_x.shape[0]], train_y, val_x_with_topo, val_y)

as x_train_model is the stacking of the original x_train with the validation features.

Mismatch between train and test features

after cloning the project, I am running FootballTDA.py.

I insert team to be analysed, player to be replaced an player to replace, but I get:

File "FootballTDA.py", line 84, in
matches_probabilities = get_probabilities(rf_model, x_test, team_ids)
File "/Volumes/Dados/Documents/Code/Apps/gato_mestre/football-tda-master/notebook_functions.py", line 151, in get_probabilities
prob_pred = model.predict_proba(x_test)
File "/Users/vitorpatalano/anaconda2/envs/ds/lib/python3.7/site-packages/sklearn/ensemble/_forest.py", line 674, in predict_proba
X = self._validate_X_predict(X)
File "/Users/vitorpatalano/anaconda2/envs/ds/lib/python3.7/site-packages/sklearn/ensemble/_forest.py", line 422, in validate_X_predict
return self.estimators[0]._validate_X_predict(X, check_input=True)
File "/Users/vitorpatalano/anaconda2/envs/ds/lib/python3.7/site-packages/sklearn/tree/_classes.py", line 403, in _validate_X_predict
reset=False)
File "/Users/vitorpatalano/anaconda2/envs/ds/lib/python3.7/site-packages/sklearn/base.py", line 437, in _validate_data
self._check_n_features(X, reset=reset)
File "/Users/vitorpatalano/anaconda2/envs/ds/lib/python3.7/site-packages/sklearn/base.py", line 366, in _check_n_features
f"X has {n_features} features, but {self.class.name} "
ValueError: X has 24 features, but DecisionTreeClassifier is expecting 19 features as input.

AM I missing something?